Ganglion Cell-inner Plexiform Research Articles

Effect of intraocular pressure reduction on progressive high myopia (PHM study): study protocol of a randomised controlled trial

BackgroundIn adult patients with high myopia (HM), progressive axial elongation poses a significant risk for the development of subsequent ocular complications that may lead to visual impairment. Effective strategies to...

Two Cases of Sudden Intraocular Pressure Elevation in Children after Steroid Treatment

Purpose: We report two cases of sudden intraocular pressure (IOP) elevation in children after steroid treatment.Case summary: (Case 1) An 8-year-old boy visited the hospital with vomiting and headache that had begun 3 days after application of dexamethasone eyedrops. IOPs were 55 and 62 mmHg in the right and left eyes, respectively; both anterior chambers were normal. The eyedrops were stopped and timolol/dorzolamide was applied twice daily. After intravenous mannitol injection, the IOPs of both eyes decreased to 18 mmHg within 24 hours. (Case 2) A 10-year-old boy diagnosed with coronavirus disease 2019 three days prior had been prescribed methylprednisolone 4 mg twice daily; he visited the hospital with headache, eye pain, and decreased vision that began 1 day after medication. IOPs were 41 and 54 mmHg in the right and left eyes, respectively; both anterior chambers were normal. After drug discontinuation, timolol/dorzolamide, brimonidine, and latanoprost eyedrops were applied. Subsequently, after intravenous mannitol injection, the IOPs decreased within 24 hours to 7 and 16 mmHg in the right and left eyes, respectively; they remained stable. However, thinning was observed in the retinal nerve fiber and ganglion cell-inner plexiform layer.Conclusions: Children can develop rapid IOP elevation after even 1 day of steroid use; residual structural damage may be present, despite prompt treatment. Clinicians must closely monitor such patients for atypical IOP elevation.

Predictive value of retinal atrophy for cognitive decline across disease duration in multiple sclerosis

BackgroundWe investigated the association between changes in retinal thickness and cognition in people with MS (PwMS), exploring the predictive value of optical coherence tomography (OCT) markers of neuroaxonal damage for...

Classifying glaucoma exclusively with OCT: comparison of three clustering algorithms derived from machine learning.

To objectively classify eyes as either healthy or glaucoma based exclusively on data provided by peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell-inner plexiform (GCIPL) measurements derived from spectral-domain optical coherence tomography (SD-OCT) using machine learning algorithms. Three clustering methods (k-means, hierarchical cluster analysis -HCA- and model-based clustering-MBC-) were used separately to classify a training sample of 109 eyes as either healthy or glaucomatous using solely 13 SD-OCT parameters: pRNFL average and sector thicknesses and GCIPL average and minimum values together with the six macular wedge-shaped regions. Then, the best-performing algorithm was applied to an independent test sample of 102 eyes to derive close estimates of its actual performance (external validation). In the training sample, accuracy was 91.7% for MBC, 81.7% for k-means and 78.9% for HCA (p value = 0.02). The best MBC model was that in which subgroups were allowed to have variable volume and shape and equal orientation. The MBC algorithm in the independent test sample correctly classified 98 out of 102 cases for an overall accuracy of 96.1% (95% CI, 92.3-99.8%), with a sensitivity of 94.3 and 100% specificity. The accuracy for pRNFL was 92.2% (95% CI, 86.9-97.4%) and for GCIPL 98.0% (95% CI, 95.3-100%). Clustering algorithms in general (and MBC in particular) seem promising methods to help discriminate between healthy and glaucomatous eyes using exclusively SD-OCT-derived parameters. Understanding the relative merits of one method over others may also provide insights into the nature of the disease.

Effects of Myopia on Rates of Change in Optical Coherence Tomography Measured Retinal Layer Thicknesses in People with Multiple Sclerosis and Healthy Controls

PurposeTo quantify the associations of myopia with longitudinal changes in retinal layer thicknesses in people with multiple sclerosis (PwMS) and healthy controls (HC).MethodsA cohort of PwMS and HC with recorded refractive error (RE) prospectively scanned on Cirrus HD-OCT at the Johns Hopkins MS Center was assessed for inclusion. Exclusion criteria included OCT follow-up < 6 months, ocular comorbidities, incidental OCT pathologies, and inadequate scan quality. Eyes were classified as having high myopia (HM) (RE≤ −6 diopters), low myopia (LM) (RE> −6 and ≤ −3 diopters), or no myopia (NM) (RE> −3 and ≤ +2.75). Linear mixed-effects regression models were used in analyses.ResultsA total of 213 PwMS (eyes: 67 HM, 98 LM, 207 NM) and 80 HC (eyes: 26 HM, 37 LM, 93 NM) were included. Baseline average ganglion cell/inner plexiform (GCIPL) and peri-papillary retinal nerve fiber layer (pRNFL) thicknesses were lower in MS HM compared with MS NM (diff: −3.2 µm, 95% CI: −5.5 to −0.8, p = 0.008 and −5.3 µm, 95% CI: −9.0 to −1.7, p = 0.004, respectively), and similarly in HC HM, as compared with HC NM. Baseline superior, inferior, and nasal pRNFL thicknesses were lower in HM compared with NM, while temporal pRNFL thickness was higher, both in MS and HC (MS: 7.1 µm, 95% CI: 2.7–11.6, p = 0.002; HC: 4.7 µm, 95% CI: −0.3 to 9.7, p = 0.07). No longitudinal differences in rates of GCIPL change were noted between HM and LM vs. NM, either in MS or HC.ConclusionCross-sectional differences in average GCIPL and pRNFL thicknesses are commonly seen in people with HM as compared to reference normative values from people with NM and can lead to false attribution of pathology if RE is not taken into account. However, our study suggests that longitudinal changes in average GCIPL thickness in PwMS with myopia are similar in magnitude to PwMS with NM, and therefore are appropriate for monitoring disease-related pathology.

The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.

Recent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. Participants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85-0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model (p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71-0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models. A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications. This study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria.

Socioeconomic disparity is associated with faster retinal neurodegeneration in multiple sclerosis.

Disease course in multiple sclerosis is notably heterogeneous, and few prognostic indicators have been consistently associated with multiple sclerosis severity. In the general population, socioeconomic disparity is associated with multimorbidity and may contribute to worse disease outcomes in multiple sclerosis. Herein, we assessed whether indicators of socioeconomic status are associated with disease progression in patients with multiple sclerosis using highly sensitive imaging tools such as optical coherence tomography, and determined whether differential multiple sclerosis management or comorbidity mediate any observed socioeconomic status-associated effects. We included 789 participants with longitudinal optical coherence tomography and low contrast letter acuity (at 1.25 and 2.5%) in whom neighbourhood- (derived via nine-digit postal codes) and participant-level socioeconomic status indicators were available ≤10 years of multiple sclerosis symptom onset. Sensitivity analyses included participants with socioeconomic status indicators available ≤3years of symptom onset (n = 552). Neighbourhood-level indicators included state and national area deprivation indices, median household income and the Agency for Healthcare Research and Quality (AHRQ) Socioeconomic Status Index. Participant-level indicators included education level. Biannual optical coherence tomography scans were segmented to quantify thickness of the composite macular ganglion cell+inner plexiform (GCIPL) layer. We assessed the association between socioeconomic status indicators and GCIPL atrophy or low contrast letter acuity loss using mixed models adjusting for demographic (including race and ethnicity) and disease-related characteristics. We also assessed socioeconomic status indicators in relation to multiple sclerosis therapy changes and comorbidity risk using survival analysis. More disadvantaged neighbourhood-level and patient-level socioeconomic status indicators were associated with faster retinal atrophy. Differences in rate of GCIPL atrophy for individuals in the top quartile (most disadvantaged) relative to the bottom quartile (least) for state area deprivation indices were -0.12 µm/year faster [95% confidence interval (CI): -0.19, -0.04; P = 0.003], for national area deprivation indices were -0.08 µm/year faster (95% CI: -0.15, -0.005; P = 0.02), for household income were -0.11 µm/year faster (95% CI: -0.19, -0.03; P = 0.008), for AHRQ Socioeconomic Status Index were -0.12 µm/year faster (95% CI: -0.19, -0.04) and for education level were -0.17 µm/year faster (95% CI: -0.26, -0.08; P = 0.0002). Similar associations were observed for socioeconomic status indicators and low contrast letter acuity loss. Lower socioeconomic status was associated with higher risk of incident comorbidity during follow-up. Low socioeconomic status individuals had faster rates of therapy escalation, suggesting the association between socioeconomic status and GCIPL atrophy may not be explained by differential contemporaneous multiple sclerosis therapy management. In conclusion, socioeconomic disparity is associated with faster retinal neurodegeneration in multiple sclerosis. As low socioeconomic status was associated with a higher risk of incident comorbidities that may adversely affect multiple sclerosis outcomes, comorbidity prevention may mitigate some of the unfavourable socioeconomic status-associated consequences.

Optical Coherence Tomography and Pattern Electroretinography Changes in Egyptian Patients with Major Depressive Disorder

Abstract Background Major depressive disorder (MDD) is a common psychiatric disorder that affects nearly 11.1-14.6 % of the population in their lifetime. Pathophysiology and brain imaging findings show that degenerative and inflammatory processes may play a role. Meta-analysis of voxel-based morphometry studies in MDD demonstrated significant gray matter loss. From anatomical and embryological perspectives, the retina can be considered a unique extension of the brain and is able to reflect axonal histopathology. Being unmyelinated, it can provide insight into the pathophysiological processes of diseases with a neurodegenerative element. Aim to compare retinal optical coherence tomography (OCT) parameters in a group of MDD patients with a healthy control group and to correlate OCT parameters with pattern electroretinography (PERG) parameters. Method a controlled cross sectional study was conducted on 30 MDD patients and 28 age and sex matched controls. Both groups had a full ophthalmological examination, OCT imaging and 7 patients and 11 controls have PERG recorded. Results Thinning of the superior retinal nerve fiber layer, thinning of most of the ganglion cell inner plexiform (GCIP) layer, thinning of most of the macular thickness and thinning of macular volume in both eyes were detected. There was a statistically significant positive correlation between the left GCIP layer and the amplitude of the N95 wave. Also a statistically significant negative correlation existed between MDD duration in years with the left eye's average volume of the outer ring of the macula. Conclusion Significant retinal changes were detected by OCT in MDD patients supporting the theory of neurodegeneration as a pathophysiology of MDD.

Detection of Longitudinal Ganglion Cell/Inner Plexiform Layer Change: Comparison of Two Spectral-Domain Optical Coherence Tomography Devices

We compared rates of change of macular ganglion cell/inner plexiform (GCIPL) thickness and proportion of worsening and improving rates from 2 optical coherence tomography (OCT) devices in a cohort of eyes with glaucoma. Longitudinal cohort study. In a tertiary glaucoma clinic we evaluated 68 glaucoma eyes with ≥2 years of follow-up and ≥4 OCT images. Macular volume scans from 2 OCT devices were exported, coregistered, and segmented. Global and sectoral GCIPL data from the central 4.8×4.0-mm region were extracted. GCIPL rates of change were estimated with linear regression. Permutation analyses were used to control specificity with the 2.5 percentile cutoff point used to define "true" worsening. Main outcome measures included differences in global/sectoral GCIPL rates of change between 2 OCT devices and the proportion of negative vs positive rates of change (P < .05). Average (standard deviation) 24-2 visual field mean deviation, median (interquartile range) follow-up time, and number of OCT images were -9.4 (6.1) dB, 3.8 (3.3-4.2) years, and 6 (5-8), respectively. GCIPL rates of thinning from Spectralis OCT were faster (more negative) compared with Cirrus OCT; differences were significant in superonasal (P=.03) and superotemporal (P=.04) sectors. A higher proportion of significant negative rates was observed with Spectralis OCT both globally and in inferotemporal/superotemporal sectors (P < .04). Permutation analyses confirmed the higher proportion of global and sectoral negative rates of change with Spectralis OCT (P < .001). Changes in macular GCIPL were detected more frequently on Spectralis' longitudinal volume scans than those of Cirrus OCT. OCT devices are not interchangeable with regard to detection of macular structural progression.

Effect of image quality fluctuations on the repeatability of thickness measurements in swept-source optical coherence tomography

This study investigated the effect of image quality fluctuations on the repeatability of thickness measurements of the peripapillary retinal nerve fibre (PP-RNFL) and ganglion cell-inner plexiform (GC-IPL) layers using swept-source optical coherence tomography (SS-OCT). Three consecutive OCT scans each were performed on 56 healthy subject. Finally, 168 SS-OCT results were analysed. Based on the tertile values of the mean absolute difference of image quality score, all subjects were divided into the following three groups—low-(LIQD), moderate-(MIQD), and high-(HIQD) image quality score difference groups. A linear mixed model and intraclass correlation coefficients (ICCs) were used for analyses. Despite high ICC values (> 0.9), several sectors showed significant differences in the ICC values in intergroup comparisons. For LIQD-HIQD and MIQD-HIQD, most PP-RNFL sectors showed significant differences. For GC-IPL sectors, the LIQD-HIQD comparison showed significant differences in the temporosuperior (p = 0.012), inferior (p < .001), and temporoinferior (p = 0.042) sectors. Significant differences existed in the average GC-IPL (p = 0.009), nasoinferior (p = 0.035), and inferior GC-IPL sectors (p < .001) for MIQD-HIQD comparison. With higher image quality fluctuations, the repeatability of SS-OCT decreased in several sectors, which are considered clinically relevant in evaluating glaucoma status. Therefore, maintaining high-quality image status is essential to enhance the reliability of SS-OCT.

Long-Term Stability of Neuroaxonal Structure in Alemtuzumab-Treated Relapsing-Remitting Multiple Sclerosis Patients.

Patients with multiple sclerosis (MS) experience progressive thinning in optical coherence tomography (OCT) measures of neuroaxonal structure regardless of optic neuritis history. Few prospective studies have investigated the effects of disease-modifying therapies on neuroaxonal degeneration in the retina. Alemtuzumab is a monoclonal antibody shown to be superior to interferon β-1a in treating relapsing-remitting MS (RRMS). The purpose of this study was to assess the effects of alemtuzumab and first-line injectable treatments on OCT measures of neuroaxonal structure including peripapillary retinal nerve fiber layer (RNFL) thickness and combined ganglion cell-inner plexiform (GCIP) layer volume in RRMS patients followed up over 5 years. In this retrospective pilot study with prospectively collected double cohort data, spectral domain OCT measures of RNFL thickness and GCIP volume were compared between alemtuzumab-treated RRMS patients (N = 24) and RRMS patients treated with either interferon-β or glatiramer acetate (N = 21). Over a median of 60 months (range 42-60 months), the alemtuzumab cohort demonstrated a change in the mean RNFL thickness (thinning from baseline) of -0.88 μm (95% confidence interval [CI] -2.63 to 0.86; P = 0.32) and mean GCIP volume of +0.013 mm (95% CI -0.006 to 0.032; P = 0.18). Over the same time period, the first-line therapy-treated cohort demonstrated greater degrees of RNFL thinning (mean change in RNFL thickness was -3.65 μm [95% CI -5.40 to -1.89; P = 0.0001]). There was also more prominent GCIP volume loss relative to baseline in the first-line therapy group (-0.052 mm [95% CI -0.070 to -0.034; P < 0.0001]). Alemtuzumab-treated patients with RRMS demonstrated relative stability of OCT-measured neuroaxonal structure compared with RRMS patients treated with either interferon-β or glatiramer acetate over a 5-year period. These findings, along with previous demonstration of improved brain atrophy rates, suggest that alemtuzumab may offer long-term preservation of neuroaxonal structure in patients with RRMS.

Cross-sectional study of smoking exposure: no differential effect on OCT metrics in a cohort of MS patients

BackgroundOptical coherence tomography (OCT) provides quantitative measures of retinal layer thickness. Cigarette smoking is a risk factor for multiple sclerosis (MS) onset and disease severity: its effects on OCT metrics have not been assessed.ObjectiveThe objective of this study was to assess the effect of smoking history on retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform (GCIP) of MS patients by OCT.Methods112 MS patients were recruited from the Brigham and Women’s Hospital. Spectralis OCT scans were acquired to measure GCIP, peripapillary RNFL, and total macular volume. Multivariable linear mixed effects regression model assessed RNFL and GCIP change with fixed effects for smoking history while adjusting for optic neuritis eye status, age, disease duration, sex, baseline EDSS, and disease modifying therapies (DMTs).ResultsSmoking histories were available for 102 patients: 46 (45.10%) had a history of smoking cigarettes and 56 (54.90%) never smoked. No statistically significant differences were found between ever-smokers and never-smokers with respect to GCIP, RNFL, and macular volume.ConclusionOur study shows no significant difference in retinal thickness between ever-smokers and never-smokers. If confirmed, this result suggests mechanistic differences between the retina and other central nervous system (CNS) compartments in response to smoking and should be noted when considering OCT as a surrogate measure of CNS activity.

Measurement of macular ganglion cell–innerplexiform layer with spectral‐domain optical coherence tomography in patients with optic nerve head drusen and papilledema

Measurement of macular ganglion cell–innerplexiform layer with spectral‐domain optical coherence tomography in patients with optic nerve head drusen and papilledema

Disease-modifying therapies modulate retinal atrophy in multiple sclerosis: A retrospective study.

To retrospectively investigate whether disease-modifying therapies (DMTs) exert differential effects on rates of retinal atrophy in relapsing-remitting multiple sclerosis (RRMS), as assessed using optical coherence tomography (OCT). A total of 402 patients with RRMS followed at the Johns Hopkins MS Center who underwent Cirrus-HD OCT were assessed for eligibility. Inclusion criteria included at least 1 year of OCT follow-up and adherence to a single DMT during the period of follow-up. Combined thickness of the ganglion cell + inner plexiform (GCIP) and other retinal layers was computed utilizing automated macular segmentation. Retinal thickness changes were analyzed using mixed-effects linear regression. The effects of glatiramer acetate (GA; n = 48), natalizumab (NAT; n = 46), and interferon-β-1a subcutaneously (IFNSC; n = 35) and intramuscularly (IFNIM; n = 28) were assessed. Baseline analyses revealed no significant differences between groups in terms of age, sex, optic neuritis history, or follow-up duration. During follow-up, relative to NAT-treated patients, IFNSC- and GA-treated patients exhibited 0.37 μm/y (p < 0.001) and 0.14 μm/y (p = 0.035) faster rates of GCIP thinning, respectively, adjusting for the interval between initiation of DMT and OCT monitoring (gap time), age, sex, relapses, and disease duration. In the IFNSC group, GCIP thinning was 1.53 μm/y faster during the first year of therapy vs during the time interval afterwards (p < 0.001). Rates of GCIP atrophy in patients with RRMS vary according to DMT utilization. Our findings support OCT for monitoring neurodegenerative treatment effects in the retina, an easily accessible tissue, and as a practical outcome measure in RRMS clinical trials.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography.

The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911-0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.

Outer retinal changes following acute optic neuritis

Background: Retinal nerve fiber and ganglion cell+inner plexiform (GCIP) layer thinning following multiple sclerosis-related acute optic neuritis (AON) is well described. However, whether AON results in changes in the inner nuclear (INL), outer plexiform (OPL), outer nuclear (ONL) and/or photoreceptor segment (PS) layers remains undetermined. Objectives: The objective of this paper is to determine if INL+OPL and/or ONL+PS changes occur following AON. Methods: Thirty-three AON patients underwent serial optical coherence tomography (OCT) and visual function testing (mean follow-up: 25 months). Longitudinal changes in retinal layer thickness were analyzed using mixed-effects linear regression. Results: Four months following AON, the mean decrease in GCIP thickness relative to baseline was 11.4% (p < 0.001). At four months, a concomitant 3.4% increase in average ONL+PS thickness was observed (p < 0.001). The percentage decrease in GCIP thickness and increase in ONL+PS thickness were strongly correlated (r = −0.70; p < 0.001). Between months 4 to 12, ONL+PS thickness declined and, at 12 months, was no longer significantly different from baseline (mean change: 0.5%; p = 0.37). Similar, albeit less robust, changes in the INL+OPL were observed. Conclusions: Following AON, dynamic changes occur in the deep retinal layers, which are proportional to GCIP thinning. These novel findings help further our understanding of the biological and/or anatomical sequelae resulting from AON.

Comparison of Point Estimates and Average Thicknesses of Retinal Layers Measured Using Manual Optical Coherence Tomography Segmentation for Quantification of Retinal Neurodegeneration in Multiple Sclerosis

Purpose: The advent of macular optical coherence tomography (OCT) segmentation has enabled the in vivo quantitative assessment of retinal axonal and neuronal subpopulations. Recent studies employing OCT in multiple sclerosis (MS) have utilized various manual macular segmentation approaches to quantify retinal layer thicknesses. We investigated whether measurements of retinal layers solely at the points of maximal macular thickness (point estimates) within the central macular B-scan are representative of the corresponding average layer thicknesses for the ganglion cell + inner plexiform (GCIP) layers, inner nuclear layer (INL), outer plexiform layer (OPL) and outer nuclear layer (ONL) in MS and healthy controls. Additionally, we examined the correlation of manual segmentation-derived measures of composite layers with average thickness measures derived from automated 3-D segmentation of the macular cube.Materials and Methods: Spectral-domain OCT central macular B-scans of 52 MS patients and 30 healthy controls (HCs) were manually segmented. Average layer thicknesses and layer thicknesses at the points of maximal macular thickness were calculated. Macular cube scans were also segmented utilizing a fully automated 3-D segmentation algorithm.Results: GCIP, INL and OPL maximal thicknesses derived from point estimates correlated well with the average thicknesses of these layers within the central macular B-scan, whereas the ONL maximal thickness did not correlate as strongly. Manual segmentation-derived point estimates and average thickness measures of the GCIP correlated excellently with corresponding automated segmentation-derived measures. MS patients had significantly decreased GCIP maximal and average thicknesses relative to HCs. ONL average thickness was significantly decreased in MS compared to HCs, but this was not true of the ONL maximal thickness.Conclusions: GCIP, INL and OPL maximal layer thicknesses may be used as surrogates to assess the gross structural integrity of these layers in MS, in a time-conservative fashion.